AI 与机器学习2018arxiv.org
玻尔兹曼编码对抗机（Boltzmann Encoded Adversarial Machines）
Boltzmann Encoded Adversarial Machines
专业 · 约 35 分钟读完前置知识：受限玻尔兹曼机、生成对抗网络、MCMC/对比散度
导读
论文提出 Boltzmann Encoded Adversarial Machines（BEAM），用受限玻尔兹曼机（RBM）替代 GAN 中的生成器网络，以利用 RBM 更稳定、更易训练的特性。实验显示 BEAM 在低维高斯混合生成任务上明显优于 GAN，在图像生成等高维任务上也有改进迹象，但高维结果较主观、仍需他人复现。
HN 关注点：能否为 GAN 训练不稳定提供替代思路，并重新唤起对玻尔兹曼机路线的兴趣。
HN 评论精选
这几条评论里：支持 1 补充 2
支持解释 BEAM 用 RBM 替代 GAN 生成器，认为低维高斯混合实验有说服力但高维结果需复现。
很有意思。从高层看（忽略很多细节），主要想法是用受限玻尔兹曼机（RBM）替换 GAN 中的生成器网络，RBM 更容易训练（更稳定）。作者称这种架构为 Boltzmann Encoded Adversarial Machines，简称 BEAM。实验提供了有说服力的证据表明 BEAM 优于 GAN。特别是图 3 我觉得很有说服力——它比较了不同架构学习生成低维高斯混合分布的能力，BEAM 明显优于 GAN。在图像生成等高维应用中的结果也表明 BEAM 优于 GAN，但由于高维数据的性质，改进多少有些主观。显然这些结果需要他人复现。对我来说看起来很有前景。话虽如此，我已经多年没碰 RBM 了——只模糊记得它们如何工作，以及如何按 Hinton 2006 年左右提出的逐层训练。该重读旧论文了！
cs702查看英文原文
Very interesting. At a high level (ignoring many details) the main idea is to replace generator networks in GANs with Restricted Boltzman Machines, or RBMs, which are easier to train (more stable). The authors call this kind of architecture "Boltzmann Encoded Adversarial Machines," or BEAM for short. The experiments provide persuasive evidence that BEAMs outperform GANs. Figure 3, in particular, I find very persuasive -- it compares the ability of different architectures to learn to generate low-dimensional mixtures of Gaussians, with BEAMs very clearly outperforming GANs. The results in higher-dimensional applications such as image generation also suggest that BEAMs outperform GANs, but the improvement is somewhat more subjective due to the nature of high-dimensional data. Obviously, these results need to be replicated by others. It looks promising to me. That said, it's been years since I've touched an RBM -- I only have a vague recollection of how they work and how they're trained, layer by layer, as proposed by Hinton in 2006 or so. Time to re-read old papers!
补充反思深度学习单用途前馈网络，感叹玻尔兹曼机研究冷清，希望 BEAM 带来新方向。
过去几年深度学习在计算机视觉等所有基准上突飞猛进，令人惊叹。但同时，所有这些都是单用途的非对称前馈网络，各自解决自己的小任务，这让人感觉有点不对劲。训练时朝一个方向，使用时朝另一个方向，然后就被丢弃。也许会被串联起来完成更复杂的任务，但对普通（真实世界应用）的深度学习网络来说，似乎也就这样了。我相信 ML 领域有很多有趣的工作在改进这种情况并提出新架构。然而最近重新发现玻尔兹曼机时，我有点惊讶地发现相关研究似乎并不多（例如 NIPS 2017 上很少？）。这个 BEAM 看起来很有意思，希望它能打开更好地理解和建模我们世界的大门。
w-m查看英文原文
It's amazing to see deep learning blast through all the benchmarks, for example in computer vision, over the last couple of years. At the same time something starts to feel off about having all these single-use asymmetric feedforward networks solving their own little task. Being trained in one direction, then used in the other, then thrown away. Maybe being chained together for a more complex task, but that seems to be about it for the average (real-world application) use case of deep learning nets. I'm sure there's plenty of interesting work being done in ML to improve on this situation and come up with new architectures. Yet I was moderately surprised when I rediscovered Boltzmann machines recently, and found not much work seemed to be going on there at all (very little at NIPS 2017 for example?). This BEAM seems intriguing, here's hoping it opens the door to a better understanding and modeling of our world.
补充设想用对抗系统检测并惩罚训练中的脆弱/过拟合，作为两阶段训练思路。
我在想，对抗系统方面的工作（这一个相当有趣）能否帮助我们解决巨大的心病——‘天哪，它过拟合了 :-('。现在我们建模、训练、测试、失败，然后从头再来，通常还要调超参数——如果把训练变成两阶段方法，每个周期用 BEAM/GAN 之类的东西来衡量反向传播有多‘脆弱’，会怎样？想法是通过在反向传播过窄时惩罚它，来削平学习模型中的尖峰——训练会更久，但我想我们会丢弃更少的数据集。
MarkMMullin查看英文原文
I'm wondering if the work on adversarial systems, this one being quite interesting, can help us with our giant bugaboo of "OMG, its overfitted :-(" Right now we model, train, test, fail, and start all over again, and usually fiddle with the hyperparameters to boot - what would happen if we turned training into a two phased approach, with a BEAM/GAN whatnot used on each cycle to measure how 'brittle' the backprop is? The idea being to round down the spikes in the learned model by penalizing the backprop when it is too narrow - training would take longer, but we'd throw away fewer sets, I'd think
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